Deploy R in online mode - create custom environment

create-custom-environment.py

# create a conda config with the require libraries

config_yaml = """
name: custom
channels:
  - conda-forge
dependencies:
  - r
  - libiconv
"""
with open("config.yaml", "w", encoding="utf-8") as f:
    f.write(config_yaml)

# create a custom software specification 
# note, you can also create a custom software specificaton using the user interface from Watson Machine Learning.

meta_props = {wml_client.package_extensions.ConfigurationMetaNames.NAME: "custom-r", wml_client.package_extensions.ConfigurationMetaNames.TYPE: "conda_yml"}
package_ext_details = wml_client.package_extensions.store(meta_props=meta_props, file_path="config.yaml")
package_ext_uid = wml_client.package_extensions.get_uid(package_ext_details)
meta_props = {
    wml_client.software_specifications.ConfigurationMetaNames.NAME: "custom-r",
    wml_client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: {"guid": wml_client.software_specifications.get_uid_by_name("runtime-22.1-py3.9")},
}
software_spec_details = wml_client.software_specifications.store(meta_props=meta_props)
software_spec_uid = wml_client.software_specifications.get_uid(software_spec_details)
wml_client.software_specifications.add_package_extension(software_spec_uid, package_ext_uid)